Reinforcement Learning (RL) has empowered Large Language Model (LLM) agents to tackle long-horizon tasks through multi-step interactions with the environment. However, current LLM agents still rely on inefficient trial-and-error exploration, leading to frequent task failures and redundant actions. In this paper, we propose MetaAct, an RL framework that incorporates a set of high-level meta-actions to explicitly guide the agent towards promising trajectories and facilitate goal-aware exploration. Specifically, the agent is required to predict one of four meta-actions (FOCUS, GUESS, RESUME, UNDO) to constrain the direction of its subsequent actions to minimize unproductive trials. More importantly, we employ group-based RL with episode-level and step-level advantages to fine-tune the agent, enabling it to identify strategic directions more accurately and generate effective task-specific actions. Experimental results demonstrate that our method consistently outperforms the state-of-the-art approaches on ALFWorld and WebShop, achieving higher success rates with fewer steps. Our code and data are publicly available at https://github.com/HavenTong/MetaAct.